The Interpolation Phase Transition in Neural Networks: Memorization and Generalization Lazy Training Published 2020-09-08 Download video MP4 360p Recommendations 1:13:58 Polynomial Time Trace Reconstruction in the Smoothed Complexity Model 1:02:50 MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention 59:00 An Introduction to Graph Neural Networks: Models and Applications 1:17:55 Optimization I 1:21:19 Why Only Us: Language and Evolution 54:49 Algorithmic Trading and Machine Learning 56:25 The Unreasonable Effectiveness of Spectral Graph Theory: A Confluence of Algorithms, Geometry & ... 1:06:01 Nonparametric Bayesian Methods: Models, Algorithms, and Applications I 1:34:56 KAN: Kolmogorov-Arnold Networks | Ziming Liu 1:08:25 The Mathematics of Lattices I 1:08:14 Cryptography: From Mathematical Magic to Secure Communication 20:45 Long Short-Term Memory (LSTM), Clearly Explained 1:08:53 Polar Codes I 1:29:45 First Update from the James Webb Space Telescope 1:27:56 Artificial Stupidity: The New AI and the Future of Fintech 16:30 Why do Convolutional Neural Networks work so well? 45:46 Geoffrey Hinton | On working with Ilya, choosing problems, and the power of intuition 1:03:45 Nonparametric Bayesian Methods: Models, Algorithms, and Applications II 58:04 Attention is all you need (Transformer) - Model explanation (including math), Inference and Training 46:09 Реальність яка шокує - Лоуренс Краусс Similar videos 48:35 Interpolation Phase Transition in Neural Networks: Memorization and Generalization 47:39 Alexander Rakhlin (MIT) -- Generalization, Interpolation, and Neural Nets 1:13:09 Lenka Zdeborova - Phase transition in regression and simple neural networks 35:26 A Mean-Field Theory of Lazy Training in Two-Layer Neural Nets: ... 39:03 Feature and Lazy Training by Mario Geiger 49:38 Maxim Raginsky: "A mean-field theory of lazy training in two-layer neural nets" 53:39 The generalization error of overparametrized models - Andrea Montanari (Stanford) MAD+ 24 Jun 2020 55:05 From overparametrized neural networks to harmonic regression 1:06:12 Andrea Montanari | Self-induced regularization from linear regression to neural networks 37:13 Beyond Lazy Training for Over-parameterized Tensor Decomposition 37:46 LMS-Bath Symposium 2020, Understanding interpolation in machine learning, Stephane Chretien 50:56 Tired with Phase Transitions - Alessandro Treves 40:13 Memorization with small neural networks 09:30 Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit 58:54 Adel Javanmard 26:50 ee53 lec03 Models of a neuron 1:09:28 Principles of Deep Representation Learning via Neural Collapse 1:02:15 Andrea Montanari - From Projection Pursuit to Interpolation Thresholds in Small Neural Networks 25:19 An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn More results